SPEAKER DETAILS

Wesley Chun

+WESLEY CHUN, MSCS, is the author of Prentice Hall's bestselling "Core Python" book series & companion videos (corepython.com), co-author of "Python Web Development with Django" (withdjango.com), and has written for Linux Journal, CNET, and InformIT. In addition to being an engineer & Developer Advocate at Google, he runs CyberWeb (cyberwebconsulting.com), a consultancy specializing in Python training. Wesley has over 25 years of programming, teaching, and writing experience, including helping create Yahoo!Mail using Python almost 2 decades ago. He has taught numerous Python courses at Cisco, Disney, VMware, UC Santa Barbara, UC Santa Cruz, & Foothill College. Wesley holds degrees in CS, Math, and Music from the University of California, and loves traveling worldwide to meet developers, whether at a technical conference, user group meeting, or on a university campus. He is a Fellow of the Python Software Foundation and can be reached on Google+ or Twitter (@wescpy).

We live in the era of big data, and modern tools have been developed to help analyze this vast amount of data as well as using data to discover new, never-before-seen patterns.Developers are using analysis tools to query data, but also machine learning to discern patterns that weren't possible before. With scikit-learn, pandas, PyTorch, and TensorFlow, the possibilities are endless. However, not everyone has the AI/ML nor math background to jump straight into tools like those. In this session, attendees will learn how to *use* machine learning through a set of APIs provided in the Google Cloud Platform.

By being a user of machine learning, developers will learn the terminology and the steps necessary to one day be able to fully build & train their own machine learning models then validate & deploy them to make predictions on new data. This session provides an easier on-ramp to working with machine learning by taking advantage of Google's pre-trained models accessible via API.

Each API will be demonstrated in Python for brevity. (Other languages are supported of course.) Finally, the AutoML feature will be discussed, allowing developers to customize and further train Google's APIs so that they're more appropriate for your data and being able to do so without a sophisticated machine learning background.